AutoResearchClaw researchclaw

Run the ResearchClaw autonomous research pipeline from a topic, config, and output directory.

install

source · Clone the upstream repo

git clone https://github.com/aiming-lab/AutoResearchClaw

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/aiming-lab/AutoResearchClaw "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.claude/skills/researchclaw" ~/.claude/skills/aiming-lab-autoresearchclaw-researchclaw && rm -rf "$T"

manifest: .claude/skills/researchclaw/SKILL.md

source content

ResearchClaw — Autonomous Research Pipeline Skill

Description

Run ResearchClaw's 23-stage autonomous research pipeline. Given a research topic, this skill orchestrates the entire research workflow: literature review → hypothesis generation → experiment design → code generation & execution → result analysis → paper writing → peer review → final export.

Trigger Conditions

Activate this skill when the user:

Asks to "research [topic]", "write a paper about [topic]", or "investigate [topic]"
Wants to run an autonomous research pipeline
Asks to generate a research paper from scratch
Mentions "ResearchClaw" by name

Instructions

Prerequisites Check

Verify config file exists:

ls config.yaml || ls config.researchclaw.example.yaml

If no

config.yaml

, create one from the example:

cp config.researchclaw.example.yaml config.yaml

Ensure the user's LLM API key is configured in
```
config.yaml
```
under
```
llm.api_key
```
or via
```
llm.api_key_env
```
environment variable.

Running the Pipeline

Option A: CLI (recommended)

researchclaw run --topic "Your research topic here" --auto-approve

Options:

```
--topic
```
/
```
-t
```
: Override the research topic from config
```
--config
```
/
```
-c
```
: Config file path (default:
```
config.yaml
```
)

--output

-o

: Output directory (default:

artifacts/rc-YYYYMMDD-HHMMSS-HASH/

)

```
--from-stage
```
: Resume from a specific stage (e.g.,
```
PAPER_OUTLINE
```
)
```
--auto-approve
```
: Auto-approve gate stages (5, 9, 20) without human input

Option B: Python API

from researchclaw.pipeline.runner import execute_pipeline
from researchclaw.config import RCConfig
from researchclaw.adapters import AdapterBundle
from pathlib import Path

config = RCConfig.load("config.yaml", check_paths=False)
results = execute_pipeline(
    run_dir=Path("artifacts/my-run"),
    run_id="research-001",
    config=config,
    adapters=AdapterBundle(),
    auto_approve_gates=True,
)

# Check results
for r in results:
    print(f"Stage {r.stage.name}: {r.status.value}")

Option C: Iterative Pipeline (multi-round improvement)

from researchclaw.pipeline.runner import execute_iterative_pipeline

results = execute_iterative_pipeline(
    run_dir=Path("artifacts/my-run"),
    run_id="research-001",
    config=config,
    adapters=AdapterBundle(),
    max_iterations=3,
    convergence_rounds=2,
)

Output Structure

After a successful run, the output directory contains:

artifacts/<run-id>/
├── stage-1/                # TOPIC_INIT outputs
├── stage-2/                # PROBLEM_DECOMPOSE outputs
├── ...
├── stage-10/
│   └── experiment.py       # Generated experiment code
├── stage-12/
│   └── runs/run-1.json     # Experiment execution results
├── stage-14/
│   ├── experiment_summary.json  # Aggregated metrics
│   └── results_table.tex        # LaTeX results table
├── stage-17/
│   └── paper_draft.md      # Full paper draft
├── stage-22/
│   └── charts/             # Generated visualizations
│       ├── metric_trajectory.png
│       └── experiment_comparison.png
└── pipeline_summary.json   # Overall pipeline status

Experiment Modes

Mode	Description	Config
`simulated`	LLM generates synthetic results (no code execution)	`experiment.mode: simulated`
`sandbox`	Execute generated code locally via subprocess	`experiment.mode: sandbox`
`ssh_remote`	Execute on remote GPU server via SSH	`experiment.mode: ssh_remote`

Troubleshooting

Config validation error: Run

researchclaw validate --config config.yaml

LLM connection failure: Check
```
llm.base_url
```
and API key
Sandbox execution failure: Verify
```
experiment.sandbox.python_path
```
exists and has numpy installed
Gate rejection: Use
```
--auto-approve
```
or manually approve at stages 5, 9, 20

Tools Required

File read/write (for config and artifacts)
Bash (for CLI execution)
No external MCP servers required for basic operation